Smart City Development with Urban Transfer Learning

Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd

Smartphone-based extendable telematic data collection app

Funding Information: We extend our heartfelt gratitude to the individuals and organizations that made this research endeavour possible. First and foremost, we would like to acknowledge the voluntary efforts of the drivers at De-prize Motors and others at Etegwe Roundabout Motor Park, Yenagoa Bayelsa State Nigeria. We also extend our sincere appreciation to Mr. Kabiru Momodu, a key collaborator in this research project. His tireless efforts in mobilizing and coordinating drivers, as well as his commitment to the project's success, played a pivotal role in data collection and use of the software in a real world scenario. Furthermore, we would like to express our gratitude to the Tertiary Education Trust Fund (TetFund) for their generous sponsorship of this research. Their support made it possible to undertake the bigger PhD research project, focusing on the use of AI/NLG-enabled mobile apps for driving Behaviour change and the promotion of safe driving practices in Nigeria.Peer reviewedPublisher PD

Comparing algorithms for aggressive driving event detection based on vehicle motion data

Aggressive driving is one of the main causes of fatal crashes. Correctly identifying aggressive driving events still represents a challenge in the literature. Furthermore, datasets available for testing the proposed approaches have some limitations since they generally (a) include only a few types of events, (b) contain data collected with only one device, and (c) are generated in drives that did not fully consider the variety of road characteristics and/or driving conditions. The main objective of this work is to compare the performance of several state-of-the-art algorithms for aggressive driving event detection (belonging to anomaly detection-, threshold- and machine learning-based categories) on multiple datasets containing sensors data collected with different devices (black-boxes and smartphones), on different vehicles and in different locations. A secondary objective is to verify whether smartphones could replace black-boxes in aggressive/non-aggressive classification tasks. To this aim, we propose the AD 2 (Aggressive Driving Detection) dataset, which contains (i) data collected using multiple devices to evaluate their influence on the algorithm performance, (ii) geographical data useful to analyze the context in which the events occurred, (iii) events recorded in different situations, and (iv) events generated by traveling the same path with aggressive and non-aggressive driving styles, in order to possibly separate the effects of driving style from those of road characteristics. Our experimental results highlighted the superiority of machine learning-based approaches and underlined the ability of smartphones to ensure a level of performance similar to that of black-boxes

Real-time head movement tracking through earables in moving vehicles

Abstract. The Internet of Things is enabling innovations in the automotive industry by expanding the capabilities of vehicles by connecting them with the cloud. One important application domain is traffic safety, which can benefit from monitoring the driver’s condition to see if they are capable of safely handling the vehicle. By detecting drowsiness, inattentiveness, and distraction of the driver it is possible to react before accidents happen. This thesis explores how accelerometer and gyroscope data collected using earables can be used to classify the orientation of the driver’s head in a moving vehicle. It is found that machine learning algorithms such as Random Forest and K-Nearest Neighbor can be used to reach fairly accurate classifications even without applying any noise reduction to the signal data. Data cleaning and transformation approaches are studied to see how the models could be improved further. This study paves the way for the development of driver monitoring systems capable of reacting to anomalous driving behavior before traffic accidents can happen

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Detecting Dynamic Security Threats in Multi-Component IoT Systems

The rising ubiquity of the Internet of Things (IoT) has heralded a new era of increasingly prolific and damaging IoT-centric security threat vectors. Fast-paced market demand for multi-featured IoT products urge companies, and their software engineers, to bring products to market quickly, often at the cost of security. Lack of proper security threat analysis tooling during development, testing, and release cycles exacerbate security concerns. In this paper, we augment a security threat analysis tool to use audit hooks, open-source information capture components, and machine learning techniques to profile dynamic wearable and IoT operations spanning multiple components during execution. Our tool encourages data-drive threat identification and analysis approaches that can help software engineers perform dynamic testing and threat analysis to mitigate code-level vulnerabilities that lead to attacks in IoT applications. Our approach is evaluated by means of a case study involving a system evaluation across several common attack vectors